Graphical element detection using a combination of user interface descriptor attributes from two or more graphical element detection techniques

ABSTRACT

Graphical element detection using a combination of user interface (UI) descriptor attributes from two or more graphical element detection techniques is disclosed. UI descriptors may be used to compare attributes for a given UI descriptor with attributes of UI elements found at runtime in the UI. At runtime, the attributes for the UI elements found in the UI can be searched for matches with attributes for a respective RPA workflow activity, and if an exact match or a match within a matching threshold is found, the UI element may be identified and interacted with accordingly.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of, and claims priority to, U.S. patent application Ser. No. 16/990,709 filed on Aug. 11, 2020. The subject matter thereof is hereby incorporated herein by reference in its entirety.

FIELD

The present invention generally relates to graphical element detection, and more specifically, to graphical element detection using a combination of user interface (UI) descriptor attributes from two or more graphical element detection techniques.

BACKGROUND

For robotic process automation (RPA) in a UI, graphical element detection may be performed using selectors, computer vision (CV), or optical character recognition (OCR) for each UI action. However, these techniques are applied individually and are not optimal for all scenarios. Accordingly, an improved approach may be beneficial.

SUMMARY

Certain embodiments of the present invention may provide solutions to the problems and needs in the art that have not yet been fully identified, appreciated, or solved by current graphical element detection technologies. For example, some embodiments of the present invention pertain to graphical element detection using a combination of UI descriptor attributes from two or more graphical element detection techniques (e.g., selectors, CV, OCR, etc.).

In an embodiment, a computer-implemented method for detecting graphical elements in a UI includes receiving, by a designer application, a selection of an activity to be configured in an RPA workflow. The computer-implemented method also includes receiving, by the designer application, selections of and/or modifications to one or more graphical element detection techniques of a plurality of graphical element detection techniques, one or more UI descriptor attributes of the graphical element detection techniques, or a combination thereof. The computer-implemented method further includes configuring the activity, by the designer application, based on the received selections of and/or modifications.

In another embodiment, a computer program is embodied on a non-transitory computer-readable medium. The program is configured to cause at least one processor to receive a selection of an activity to be configured in an RPA workflow. The computer program is also configured to cause the at least one processor to receive selections of and/or modifications to one or more graphical element detection techniques of a plurality of graphical element detection techniques, one or more UI descriptor attributes of the graphical element detection techniques, or a combination thereof. The computer program is further configured to cause the at least one processor to configure the activity based on the received selections of and/or modifications.

In yet another embodiment, a computer program is embodied on a non-transitory computer-readable medium. The program is configured to cause at least one processor to analyze a UI at runtime to identify UI element attributes. The computer program is also configured to cause the at least one processor to compare the UI element attributes to UI descriptor attributes from a plurality of graphical element detection techniques for an activity of an RPA workflow. When a UI element matching the attributes of the plurality of graphical element detection techniques is found via an exact match or a threshold match, the computer program is configured to cause the at least one processor to take an action associated with the activity involving the UI element.

BRIEF DESCRIPTION OF THE DRAWINGS

In order that the advantages of certain embodiments of the invention will be readily understood, a more particular description of the invention briefly described above will be rendered by reference to specific embodiments that are illustrated in the appended drawings. While it should be understood that these drawings depict only typical embodiments of the invention and are not therefore to be considered to be limiting of its scope, the invention will be described and explained with additional specificity and detail through the use of the accompanying drawings, in which:

FIG. 1 is an architectural diagram illustrating a robotic process automation (RPA) system, according to an embodiment of the present invention.

FIG. 2 is an architectural diagram illustrating a deployed RPA system, according to an embodiment of the present invention.

FIG. 3 is an architectural diagram illustrating the relationship between a designer, activities, and drivers, according to an embodiment of the present invention.

FIG. 4 is an architectural diagram illustrating an RPA system, according to an embodiment of the present invention.

FIG. 5 is an architectural diagram illustrating a computing system configured to perform graphical element detection using a combination of UI descriptor attributes from two or more graphical element detection techniques, according to an embodiment of the present invention.

FIG. 6 illustrates an example graphical element detection technique configuration interface for an RPA designer application, according to an embodiment of the present invention.

FIGS. 7A and 7B illustrate another example graphical element detection technique configuration interface for an RPA designer application, according to an embodiment of the present invention.

FIG. 8 is a flowchart illustrating a process for configuring an RPA workflow to perform graphical element detection using a combination of UI descriptor attributes from two or more graphical element detection techniques, according to an embodiment of the present invention.

FIG. 9 is a flowchart illustrating a process for performing graphical element detection using a combination of UI descriptor attributes from two or more graphical element detection techniques, according to an embodiment of the present invention.

Unless otherwise indicated, similar reference characters denote corresponding features consistently throughout the attached drawings.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Some embodiments pertain to graphical element detection using a combination of UI descriptor attributes from two or more graphical element detection techniques (e.g., selectors, CV, OCR, etc.). “Graphical elements” and “UT elements” are used interchangeably herein. At their core, UI descriptors identify the UI elements (e.g., text fields, buttons, labels, menus, checkboxes, etc.). Some types of UI descriptors include, but are not limited to, selectors, CV descriptors, image matching descriptors, OCR descriptors, unified target descriptors that may utilize multiple different types of UI descriptors in series or in parallel, etc. UI descriptors may be used to compare attributes for a given UI descriptor with attributes of UI elements found at runtime in the UI.

In some embodiments, UI descriptors store the attributes of the respective UI element and its parents, e.g., in an Extensible Markup Language (XML) fragment. At runtime, the attributes for the UI elements found in the UI can be searched for matches with attributes for a respective RPA workflow activity, and if an exact match or a “close enough” match is found within a matching threshold, the UI element may be identified and interacted with accordingly. The attributes may include text-based identifiers (IDs), classes, roles, and the like. For CV, the attributes may include the type of the target element and the relation to one or more anchor elements that may be used in a multi-anchor matching approach. For OCR, the attributes may include text in the form of a stored string, for example, and text found via OCR to which the stored string was fuzzy matched during execution. Any suitable attributes and graphical element detection techniques may be used without deviating from the scope of the invention.

Some embodiments search for a mix of automatically or custom-defined attributes from two or more different graphical element detection techniques. For instance, some selector attributes, some CV descriptor attributes, and some OCR descriptor attributes may be used to collectively, and potentially simultaneously, identify the graphical element at runtime in a manner that may be more accurate than using any graphical element detection technique alone. For example, if there are various groups of checkboxes on a screen with different visual appearances, it may make sense to use a combination of an image matching descriptor to identify the checkbox image and a CV descriptor that provides multi-anchor matching information pertaining to the location of the specific checkbox in relation to nearby anchor UI elements. This may increase accuracy over a single graphical element detection technique alone.

In some embodiments, the combined graphical element detection technique may be configured at design time using an RPA designer application (e.g., UiPath Studio™). For instance, an out-of-the-box click activity could be configured to look at a type of a target graphical element to be identified and/or certain characteristics of the target graphical element to be identified to automatically determine what combination of graphical element detection techniques and respective attributes to use. In certain embodiments, the RPA developer may modify the default logic for the activity and/or provide the graphical element detection techniques and attributes thereof to be used manually. For instance, the RPA developer may select the types of graphical element detection techniques to apply. In some embodiments, the RPA developer may select from a list of attributes for the given techniques to select from, the developer may enter the attributes manually, etc.

The combination of attributes used from these techniques can be a subset and/or all of the attributes for each technique. For instance, in some embodiments, all attributes for all selected techniques may be used. However, in certain embodiments, one attribute from a selector may be used, two attributes from a CV descriptor may be used, all attributes from an OCR descriptor may be used, etc.

As used herein, a “screen” is an image of an application UI or a portion of the application UI at a certain point in time. In some embodiments, UI elements and screens may be further differentiated into specific types of UI elements (e.g., buttons, checkboxes, text fields, etc.) and screens (e.g., top windows, modal windows, popup windows, etc.).

Some embodiments use UI descriptors that store the attributes of a UI element and its parents in an XML fragment. In modern computing systems, the operating system typically represents each user interface as a hierarchical data structure that is commonly referred to as a UI tree. An example UI tree may include a document object model (DOM) underlying a webpage rendered by a web browser application.

Selectors are a type to UI descriptor that may be used to detect UI elements in some embodiments. A selector has the following structure in some embodiments:

 < node_1/ > node_2/>  …   < node_N/>

The last node represents the GUI element of interest, and all previous nodes represent the parents of that element. <node_1> is usually referred to as a root node, and represents the top window of the application.

Each node may have one or more attributes that assist with correct identification of a specific level of the selected application. Each node has the following format in some embodiments:

 < ui_system  attr_name_1=^(′)attr_value_1^(′)  …  attr_name_N=^(′)attr_value_N^(′)/>

Every attribute may have an assigned value, and attributes with constant values may be selected. This is because changes to the value of an attribute each time the application is started may lead to the selector not being able to correctly identify the associated element.

A UI descriptor is a set of instructions for finding a UI element. UI descriptors in some embodiments are an encapsulated data/struct format that includes UI element selector(s), anchor selector(s), CV descriptor(s), OCR descriptor(s), unified target descriptor(s) combining two or more types of UI descriptors, a screen image capture (context), an element image capture, other metadata (e.g., the application and application version), a combination thereof, etc. The encapsulated data/struct format may be extensible with future updates to the platform and is not limited to the above definition. Any suitable UI descriptor for identifying a UI element on a screen may be used without deviating from the scope of the invention. UI descriptors may be extracted from activities in an RPA workflow and added to a structured schema that groups the UI descriptors by UI applications, screens, and UI elements.

The UI descriptors may work with a unified target that encompasses multiple or all UI detection mechanisms through which image detection and definition are performed in some embodiments. The unified target may merge multiple techniques of identifying and automating UI elements into a single cohesive approach. A unified target descriptor chains together multiple types of UI descriptors in series, uses them in parallel, or uses at least one technique (e.g., a selector) first for a period of time and then runs at least one other technique in parallel if the first technique does not find a match within the time period. In some embodiments, a unified target descriptor may function like a finite state machine (FSM), where in a first context, a first UI descriptor mechanism is applied, in a second context, a second UI descriptor is applied, etc. The unified target may prioritize selector-based and driver-based UI detection mechanisms and fall back on CV, image matching, and/or other mechanisms to find a graphical element if the first two mechanisms are not successful in some embodiments.

In some embodiments, fuzzy matching may be employed, where one or more attributes should match with a certain accuracy (e.g., a 70% match, an 80% match, a 99% match, etc.), within a certain range, using string metrics (e.g., a Levenshtein distance, a Hamming distance, a Jaro-Winkler distance, etc.), a combination thereof, etc. One of ordinary skill in the art will appreciate that the similarity measure may quantify an amount of similarity, as well as an amount of mismatch between two attribute values. Furthermore, in various embodiments, the similarity threshold may represent a maximum amount of mismatch or a minimum amount of similarity required for a match.

Depending on the chosen manner of computing the similarity measure, the similarity threshold can have various interpretations. For instance, the similarity threshold may indicate a maximum count of characters that can differ between the two strings or a fractional degree of mismatch calculated as a proportion of the total count of characters (e.g., combined string length). In some embodiments, the similarity threshold may be re-scaled to a predetermined interval, such as between 0 and 1, between 0 and 100, between 7 and 34, etc. In one nonlimiting example, a relatively high similarity threshold (e.g., close to 1 or 100%) indicates a requirement for an almost exact match, i.e., the value of the fuzzy attribute in the runtime target is only allowed to depart very slightly from the value of the respective attribute in the design time target. In contrast, when the similarity threshold is relatively low (e.g., close to 0), almost any values of the respective fuzzy attribute are considered as matching.

In certain embodiments, the matching tolerance may differ on a per-attribute basis. For instance, an exact match may be required for one or more attributes (e.g., it may be desired to find a certain exact name) and fuzzy matching may be performed for one or more other attributes. The number and/or type of attributes used from each graphical element detection technique may be custom-specified by the RPA developer in some embodiments.

In some embodiments, attributes may be stored as attribute-value pairs and/or attribute-value-tolerance pairs (e.g., fuzzy matching). Attribute-value pairs may indicate a name and a type of UI element represented by the respective node in some embodiments. However, one skilled in the art will appreciate that there may be multiple ways to represent a location of a specific node within a UI tree other than a list of attribute-value pairs without deviating from the scope of the invention.

These attribute-value pairs and/or attribute-value-tolerance pairs may be stored in a tag in some embodiments, and each tag may include a sequence of characters with the sequence book-ended by implementation-specific delimiters (e.g., beginning with “<” and ending with “/>”). Attribute-value pairs may indicate a name and a type of UI element represented by the respective node in some embodiments. However, one skilled in the art will appreciate that there may be multiple ways to represent a location of a specific node within a UI tree other than a list of attribute-value pairs without deviating from the scope of the invention.

To enable a successful and ideally unambiguous identification by an RPA robot, some embodiments represent each UI element using an element ID characterizing the respective UI element. The element ID in some embodiments indicates a location of a target node within a UI tree, where the target node represents the respective UI element. For instance, the element ID may identify a target node/UI element as a member of a selected subset of nodes. The selected subset of nodes may form a genealogy, i.e., a line of descent through the UI tree where each node is either an ancestor or a descendant of another node.

In some embodiments, the element ID includes an ordered sequence of node indicators, the sequence tracing a genealogical path through the UI tree, and the path ending in the respective target node/UI element. Each node indicator may represent a member of an object hierarchy of the respective UI and its position within the sequence consistent with the respective hierarchy. For instance, each member of the sequence may represent a descendant (e.g., a child node) of the previous member, and may have the following member as a descendant (e.g., a child node). In one HyperText Markup Language (HTML) example, an element ID representing an individual form field may indicate that the respective form field is a child of an HTML form, which in turn is a child of a specific section of a webpage, etc. The genealogy does not need to be complete in some embodiments.

Some embodiments may use one or more multi-anchor matching attributes. Anchors are other UI elements that can be used to assist in uniquely identifying a target UI element. For instance, if multiple text fields are included in a UI, searching for a text field alone is insufficient to uniquely identify a given text field. Accordingly, some embodiments look for additional information in order to uniquely identify a given UI element. Using the text field example, a text field for entering a first name may appear to the right of the label “First Name”. This first name label may be set as an “anchor” to help to uniquely identify the text field, which is the “target”.

Various positional and/or geometric associations between the target and the anchor may be used in some embodiments, potentially within one or more tolerances, to uniquely identify the target. For instance, the center of bounding boxes for the anchor and the target may be used to define a line segment. This line segment could then be required to have a certain length within a tolerance and/or slope within a tolerance to uniquely identify the target using the target/anchor pair. However, any desired position of the location associated with the target and/or anchors may be used in some embodiments without deviating from the scope of the invention. For instance, the point for drawing line segments may be in the center, upper left corner, upper right corner, lower left corner, lower right corner, any other location on the border of the bounding box, any location within the bounding box, a location outside of the bounding box as identified in relation to the bounding box properties, etc. In certain embodiments, the target and one or more anchors may have different locations within or outside of their bounding boxes that are used for geometric matching.

Per the above, a single anchor may not always be sufficient to uniquely identify a target element on a screen with a certain confidence. For instance, consider a web form where two text field for entering a first name appear to the right of respective labels “First Name” in different locations on the screen. In this example, one or more additional anchors may be useful to uniquely identify a given target. The geometric properties between the anchors and the target (e.g., line segment lengths, angles, and/or relative locations with tolerances) may be used to uniquely identify the target. The user may be required to continue to add anchors until a match strength for the target exceeds the threshold.

As used herein, the terms “user” and “developer” are used interchangeably. The user/developer may or may not have programming and/or technical knowledge. For instance, in some embodiments, the user/developer may create RPA workflows by configuring activities in the RPA workflow without manual coding. In certain embodiments, this may be done by clicking and dragging and dropping various features, for example.

Certain embodiments may be employed for robotic process automation (RPA). FIG. 1 is an architectural diagram illustrating an RPA system 100, according to an embodiment of the present invention. RPA system 100 includes a designer 110 that allows a developer to design and implement workflows. Designer 110 may provide a solution for application integration, as well as automating third-party applications, administrative Information Technology (IT) tasks, and business IT processes. Designer 110 may facilitate development of an automation project, which is a graphical representation of a business process. Simply put, designer 110 facilitates the development and deployment of workflows and robots.

The automation project enables automation of rule-based processes by giving the developer control of the execution order and the relationship between a custom set of steps developed in a workflow, defined herein as “activities.” One commercial example of an embodiment of designer 110 is UiPath Studio™. Each activity may include an action, such as clicking a button, reading a file, writing to a log panel, etc. In some embodiments, workflows may be nested or embedded.

Some types of workflows may include, but are not limited to, sequences, flowcharts, FSMs, and/or global exception handlers. Sequences may be particularly suitable for linear processes, enabling flow from one activity to another without cluttering a workflow. Flowcharts may be particularly suitable to more complex business logic, enabling integration of decisions and connection of activities in a more diverse manner through multiple branching logic operators. FSMs may be particularly suitable for large workflows. FSMs may use a finite number of states in their execution, which are triggered by a condition (i.e., transition) or an activity. Global exception handlers may be particularly suitable for determining workflow behavior when encountering an execution error and for debugging processes.

Once a workflow is developed in designer 110, execution of business processes is orchestrated by conductor 120, which orchestrates one or more robots 130 that execute the workflows developed in designer 110. One commercial example of an embodiment of conductor 120 is UiPath Orchestrator™. Conductor 120 facilitates management of the creation, monitoring, and deployment of resources in an environment. Conductor 120 may act as an integration point, or one of the aggregation points, with third-party solutions and applications.

Conductor 120 may manage a fleet of robots 130, connecting and executing robots 130 from a centralized point. Types of robots 130 that may be managed include, but are not limited to, attended robots 132, unattended robots 134, development robots (similar to unattended robots 134, but used for development and testing purposes), and nonproduction robots (similar to attended robots 132, but used for development and testing purposes). Attended robots 132 may be triggered by user events or be scheduled to automatically happen, and operate alongside a human on the same computing system. Attended robots 132 may be used with conductor 120 for a centralized process deployment and logging medium. Attended robots 132 may help the human user accomplish various tasks, and may be triggered by user events. In some embodiments, processes cannot be started from conductor 120 on this type of robot and/or they cannot run under a locked screen. In certain embodiments, attended robots 132 can only be started from a robot tray or from a command prompt. Attended robots 132 should run under human supervision in some embodiments.

Unattended robots 134 run unattended in virtual environments or on physical machines, and can automate many processes. Unattended robots 134 may be responsible for remote execution, monitoring, scheduling, and providing support for work queues. Debugging for all robot types may be run from designer 110 in some embodiments. Both attended and unattended robots may automate various systems and applications including, but not limited to, mainframes, web applications, VMs, enterprise applications (e.g., those produced by SAP®, SalesForce®, Oracle®, etc.), and computing system applications (e.g., desktop and laptop applications, mobile device applications, wearable computer applications, etc.).

Conductor 120 may have various capabilities including, but not limited to, provisioning, deployment, versioning, configuration, queueing, monitoring, logging, and/or providing interconnectivity. Provisioning may include creating and maintenance of connections between robots 130 and conductor 120 (e.g., a web application). Deployment may include assuring the correct delivery of package versions to assigned robots 130 for execution. Versioning may include management of unique instances of some process or configuration in some embodiments. Configuration may include maintenance and delivery of robot environments and process configurations. Queueing may include providing management of queues and queue items. Monitoring may include keeping track of robot identification data and maintaining user permissions. Logging may include storing and indexing logs to a database (e.g., an SQL database) and/or another storage mechanism (e.g., ElasticSearch®, which provides the ability to store and quickly query large datasets). Conductor 120 may provide interconnectivity by acting as the centralized point of communication for third-party solutions and/or applications.

Robots 130 are execution agents that run workflows built in designer 110. One commercial example of some embodiments of robot(s) 130 is UiPath Robots™ In some embodiments, robots 130 install the Microsoft Windows® Service Control Manager (SCM)-managed service by default. As a result, such robots 130 can open interactive Windows® sessions under the local system account, and have the rights of a Windows® service.

In some embodiments, robots 130 can be installed in a user mode. For such robots 130, this means they have the same rights as the user under which a given robot 130 has been installed. This feature may also be available for High Density (HD) robots, which ensure full utilization of each machine at its maximum potential. In some embodiments, any type of robot 130 may be configured in an HD environment.

Robots 130 in some embodiments are split into several components, each being dedicated to a particular automation task. The robot components in some embodiments include, but are not limited to, SCM-managed robot services, user mode robot services, executors, agents, and command line. SCM-managed robot services manage and monitor Windows® sessions and act as a proxy between conductor 120 and the execution hosts (i.e., the computing systems on which robots 130 are executed). These services are trusted with and manage the credentials for robots 130. A console application is launched by the SCM under the local system.

User mode robot services in some embodiments manage and monitor Windows® sessions and act as a proxy between conductor 120 and the execution hosts. User mode robot services may be trusted with and manage the credentials for robots 130. A Windows® application may automatically be launched if the SCM-managed robot service is not installed.

Executors may run given jobs under a Windows® session (i.e., they may execute workflows. Executors may be aware of per-monitor dots per inch (DPI) settings. Agents may be Windows® Presentation Foundation (WPF) applications that display the available jobs in the system tray window. Agents may be a client of the service. Agents may request to start or stop jobs and change settings. The command line is a client of the service. The command line is a console application that can request to start jobs and waits for their output.

Having components of robots 130 split as explained above helps developers, support users, and computing systems more easily run, identify, and track what each component is executing. Special behaviors may be configured per component this way, such as setting up different firewall rules for the executor and the service. The executor may always be aware of DPI settings per monitor in some embodiments. As a result, workflows may be executed at any DPI, regardless of the configuration of the computing system on which they were created. Projects from designer 110 may also be independent of browser zoom level in some embodiments. For applications that are DPI-unaware or intentionally marked as unaware, DPI may be disabled in some embodiments.

FIG. 2 is an architectural diagram illustrating a deployed RPA system 200, according to an embodiment of the present invention. In some embodiments, RPA system 200 may be, or may be a part of, RPA system 100 of FIG. 1. It should be noted that the client side, the server side, or both, may include any desired number of computing systems without deviating from the scope of the invention. On the client side, a robot application 210 includes executors 212, an agent 214, and a designer 216. However, in some embodiments, designer 216 may not be running on computing system 210. Executors 212 are running processes. Several business projects may run simultaneously, as shown in FIG. 2. Agent 214 (e.g., a Windows® service) is the single point of contact for all executors 212 in this embodiment. All messages in this embodiment are logged into conductor 230, which processes them further via database server 240, indexer server 250, or both. As discussed above with respect to FIG. 1, executors 212 may be robot components.

In some embodiments, a robot represents an association between a machine name and a username. The robot may manage multiple executors at the same time. On computing systems that support multiple interactive sessions running simultaneously (e.g., Windows® Server 2012), multiple robots may be running at the same time, each in a separate Windows® session using a unique username. This is referred to as HD robots above.

Agent 214 is also responsible for sending the status of the robot (e.g., periodically sending a “heartbeat” message indicating that the robot is still functioning) and downloading the required version of the package to be executed. The communication between agent 214 and conductor 230 is always initiated by agent 214 in some embodiments. In the notification scenario, agent 214 may open a WebSocket channel that is later used by conductor 230 to send commands to the robot (e.g., start, stop, etc.).

On the server side, a presentation layer (web application 232, Open Data Protocol (OData) Representative State Transfer (REST) Application Programming Interface (API) endpoints 234, and notification and monitoring 236), a service layer (API implementation/business logic 238), and a persistence layer (database server 240 and indexer server 250) are included. Conductor 230 includes web application 232, OData REST API endpoints 234, notification and monitoring 236, and API implementation/business logic 238. In some embodiments, most actions that a user performs in the interface of conductor 230 (e.g., via browser 220) are performed by calling various APIs. Such actions may include, but are not limited to, starting jobs on robots, adding/removing data in queues, scheduling jobs to run unattended, etc. without deviating from the scope of the invention. Web application 232 is the visual layer of the server platform. In this embodiment, web application 232 uses Hypertext Markup Language (HTML) and JavaScript (JS). However, any desired markup languages, script languages, or any other formats may be used without deviating from the scope of the invention. The user interacts with web pages from web application 232 via browser 220 in this embodiment in order to perform various actions to control conductor 230. For instance, the user may create robot groups, assign packages to the robots, analyze logs per robot and/or per process, start and stop robots, etc.

In addition to web application 232, conductor 230 also includes service layer that exposes OData REST API endpoints 234. However, other endpoints may be included without deviating from the scope of the invention. The REST API is consumed by both web application 232 and agent 214. Agent 214 is the supervisor of one or more robots on the client computer in this embodiment.

The REST API in this embodiment covers configuration, logging, monitoring, and queueing functionality. The configuration endpoints may be used to define and configure application users, permissions, robots, assets, releases, and environments in some embodiments. Logging REST endpoints may be used to log different information, such as errors, explicit messages sent by the robots, and other environment-specific information, for instance. Deployment REST endpoints may be used by the robots to query the package version that should be executed if the start job command is used in conductor 230. Queueing REST endpoints may be responsible for queues and queue item management, such as adding data to a queue, obtaining a transaction from the queue, setting the status of a transaction, etc.

Monitoring REST endpoints may monitor web application 232 and agent 214. Notification and monitoring API 236 may be REST endpoints that are used for registering agent 214, delivering configuration settings to agent 214, and for sending/receiving notifications from the server and agent 214. Notification and monitoring API 236 may also use WebSocket communication in some embodiments.

The persistence layer includes a pair of servers in this embodiment—database server 240 (e.g., a SQL server) and indexer server 250. Database server 240 in this embodiment stores the configurations of the robots, robot groups, associated processes, users, roles, schedules, etc. This information is managed through web application 232 in some embodiments. Database server 240 may manages queues and queue items. In some embodiments, database server 240 may store messages logged by the robots (in addition to or in lieu of indexer server 250).

Indexer server 250, which is optional in some embodiments, stores and indexes the information logged by the robots. In certain embodiments, indexer server 250 may be disabled through configuration settings. In some embodiments, indexer server 250 uses ElasticSearch®, which is an open source project full-text search engine. Messages logged by robots (e.g., using activities like log message or write line) may be sent through the logging REST endpoint(s) to indexer server 250, where they are indexed for future utilization.

FIG. 3 is an architectural diagram illustrating the relationship 300 between a designer 310, activities 320, 330, and drivers 340, according to an embodiment of the present invention. Per the above, a developer uses designer 310 to develop workflows that are executed by robots. Workflows may include user-defined activities 320 and UI automation activities 330. Some embodiments are able to identify non-textual visual components in an image, which is called computer vision (CV) herein. Some CV activities pertaining to such components may include, but are not limited to, click, type, get text, hover, element exists, refresh scope, highlight, etc. Click in some embodiments identifies an element using CV, optical character recognition (OCR), fuzzy text matching, and multi-anchor, for example, and clicks it. Type may identify an element using the above and types in the element. Get text may identify the location of specific text and scan it using OCR. Hover may identify an element and hover over it. Element exists may check whether an element exists on the screen using the techniques described above. In some embodiments, there may be hundreds or even thousands of activities that can be implemented in designer 310. However, any number and/or type of activities may be available without deviating from the scope of the invention.

UI automation activities 330 are a subset of special, lower level activities that are written in lower level code (e.g., CV activities) and facilitate interactions with applications through the UI layer. In certain embodiments, UI automation activities 300 may simulate” user input through window messages or the like, for example. UI automation activities 330 facilitate these interactions via drivers 340 that allow the robot to interact with the desired software. For instance, drivers 340 may include OS drivers 342, browser drivers 344, VM drivers 346, enterprise application drivers 348, etc.

Drivers 340 may interact with the OS at a low level looking for hooks, monitoring for keys, etc. They may facilitate integration with Chrome®, IE®, Citrix®, SAP®, etc. For instance, the “click” activity performs the same role in these different applications via drivers 340.

FIG. 4 is an architectural diagram illustrating an RPA system 400, according to an embodiment of the present invention. In some embodiments, RPA system 400 may be or include RPA systems 100 and/or 200 of FIGS. 1 and/or 2. RPA system 400 includes multiple client computing systems 410 running robots. Computing systems 410 are able to communicate with a conductor computing system 420 via a web application running thereon. Conductor computing system 420, in turn, is able to communicate with a database server 430 and an optional indexer server 440.

With respect to FIGS. 1 and 3, it should be noted that while a web application is used in these embodiments, any suitable client and/or server software may be used without deviating from the scope of the invention. For instance, the conductor may run a server-side application that communicates with non-web-based client software applications on the client computing systems.

FIG. 5 is an architectural diagram illustrating a computing system 500 configured to perform graphical element detection using a combination of UI descriptor attributes from two or more graphical element detection techniques, according to an embodiment of the present invention. In some embodiments, computing system 500 may be one or more of the computing systems depicted and/or described herein. Computing system 500 includes a bus 505 or other communication mechanism for communicating information, and processor(s) 510 coupled to bus 505 for processing information. Processor(s) 510 may be any type of general or specific purpose processor, including a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a Graphics Processing Unit (GPU), multiple instances thereof, and/or any combination thereof. Processor(s) 510 may also have multiple processing cores, and at least some of the cores may be configured to perform specific functions. Multi-parallel processing may be used in some embodiments. In certain embodiments, at least one of processor(s) 510 may be a neuromorphic circuit that includes processing elements that mimic biological neurons. In some embodiments, neuromorphic circuits may not require the typical components of a Von Neumann computing architecture.

Computing system 500 further includes a memory 515 for storing information and instructions to be executed by processor(s) 510. Memory 515 can be comprised of any combination of Random Access Memory (RAM), Read Only Memory (ROM), flash memory, cache, static storage such as a magnetic or optical disk, or any other types of non-transitory computer-readable media or combinations thereof. Non-transitory computer-readable media may be any available media that can be accessed by processor(s) 510 and may include volatile media, non-volatile media, or both. The media may also be removable, non-removable, or both.

Additionally, computing system 500 includes a communication device 520, such as a transceiver, to provide access to a communications network via a wireless and/or wired connection. In some embodiments, communication device 520 may be configured to use Frequency Division Multiple Access (FDMA), Single Carrier FDMA (SC-FDMA), Time Division Multiple Access (TDMA), Code Division Multiple Access (CDMA), Orthogonal Frequency Division Multiplexing (OFDM), Orthogonal Frequency Division Multiple Access (OFDMA), Global System for Mobile (GSM) communications, General Packet Radio Service (GPRS), Universal Mobile Telecommunications System (UMTS), cdma2000, Wideband CDMA (W-CDMA), High-Speed Downlink Packet Access (HSDPA), High-Speed Uplink Packet Access (HSUPA), High-Speed Packet Access (HSPA), Long Term Evolution (LTE), LTE Advanced (LTE-A), 802.11x, Wi-Fi, Zigbee, Ultra-WideBand (UWB), 802.16x, 802.15, Home Node-B (HnB), Bluetooth, Radio Frequency Identification (RFID), Infrared Data Association (IrDA), Near-Field Communications (NFC), fifth generation (5G), New Radio (NR), any combination thereof, and/or any other currently existing or future-implemented communications standard and/or protocol without deviating from the scope of the invention. In some embodiments, communication device 520 may include one or more antennas that are singular, arrayed, phased, switched, beamforming, beamsteering, a combination thereof, and or any other antenna configuration without deviating from the scope of the invention.

Processor(s) 510 are further coupled via bus 505 to a display 525, such as a plasma display, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, a Field Emission Display (FED), an Organic Light Emitting Diode (OLED) display, a flexible OLED display, a flexible substrate display, a projection display, a 4K display, a high definition display, a Retina® display, an In-Plane Switching (IPS) display, or any other suitable display for displaying information to a user. Display 525 may be configured as a touch (haptic) display, a three dimensional (3D) touch display, a multi-input touch display, a multi-touch display, etc. using resistive, capacitive, surface-acoustic wave (SAW) capacitive, infrared, optical imaging, dispersive signal technology, acoustic pulse recognition, frustrated total internal reflection, etc. Any suitable display device and haptic I/O may be used without deviating from the scope of the invention.

A keyboard 530 and a cursor control device 535, such as a computer mouse, a touchpad, etc., are further coupled to bus 505 to enable a user to interface with computing system 500. However, in certain embodiments, a physical keyboard and mouse may not be present, and the user may interact with the device solely through display 525 and/or a touchpad (not shown). Any type and combination of input devices may be used as a matter of design choice. In certain embodiments, no physical input device and/or display is present. For instance, the user may interact with computing system 500 remotely via another computing system in communication therewith, or computing system 500 may operate autonomously.

Memory 515 stores software modules that provide functionality when executed by processor(s) 510. The modules include an operating system 540 for computing system 500. The modules further include a combined technique graphical element detection module 545 that is configured to perform all or part of the processes described herein or derivatives thereof. Computing system 500 may include one or more additional functional modules 550 that include additional functionality.

One skilled in the art will appreciate that a “system” could be embodied as a server, an embedded computing system, a personal computer, a console, a personal digital assistant (PDA), a cell phone, a tablet computing device, a quantum computing system, or any other suitable computing device, or combination of devices without deviating from the scope of the invention. Presenting the above-described functions as being performed by a “system” is not intended to limit the scope of the present invention in any way, but is intended to provide one example of the many embodiments of the present invention. Indeed, methods, systems, and apparatuses disclosed herein may be implemented in localized and distributed forms consistent with computing technology, including cloud computing systems.

It should be noted that some of the system features described in this specification have been presented as modules, in order to more particularly emphasize their implementation independence. For example, a module may be implemented as a hardware circuit comprising custom very large scale integration (VLSI) circuits or gate arrays, off-the-shelf semiconductors such as logic chips, transistors, or other discrete components. A module may also be implemented in programmable hardware devices such as field programmable gate arrays, programmable array logic, programmable logic devices, graphics processing units, or the like.

A module may also be at least partially implemented in software for execution by various types of processors. An identified unit of executable code may, for instance, include one or more physical or logical blocks of computer instructions that may, for instance, be organized as an object, procedure, or function. Nevertheless, the executables of an identified module need not be physically located together, but may include disparate instructions stored in different locations that, when joined logically together, comprise the module and achieve the stated purpose for the module. Further, modules may be stored on a computer-readable medium, which may be, for instance, a hard disk drive, flash device, RAM, tape, and/or any other such non-transitory computer-readable medium used to store data without deviating from the scope of the invention.

Indeed, a module of executable code could be a single instruction, or many instructions, and may even be distributed over several different code segments, among different programs, and across several memory devices. Similarly, operational data may be identified and illustrated herein within modules, and may be embodied in any suitable form and organized within any suitable type of data structure. The operational data may be collected as a single data set, or may be distributed over different locations including over different storage devices, and may exist, at least partially, merely as electronic signals on a system or network.

Per the above, some embodiments employ attributes from two or more graphical element detection techniques to identify graphical elements in a UI. These techniques may be configured for RPA workflow activities at design time in an RPA designer application in some embodiments. FIG. 6A illustrates an example graphical element detection technique configuration interface for an RPA designer application 600, according to an embodiment of the present invention. RPA designer application 600 includes an RPA workflow development pane 610. When a user clicks an activity that interacts with a graphical element in a UI, a graphical element detection technique configuration pane 620 shows possible graphical element detection techniques and respective attributes for that activity.

Graphical element detection technique configuration pane 620 provides the RPA developer with the ability to select from among multiple graphical element detection techniques and their associated attributes. A triangle 622 allows the RPA developer to show or hide a list of attributes for each graphical element detection technique. A graphical element detection technique selection checkbox 624 allows the RPA developer to select or de-select all of the attributes for a given graphical element detection technique at once. An attribute checkbox 626 allows the RPA developer to select or de-select a respective attribute.

In some embodiments, fuzzy matching may be employed for the attributes. By selecting a fuzzy matching radio button 628, the RPA developer can cause a respective fuzzy matching threshold field 629 to appear. The RPA developer can then specify a threshold for fuzzy matching. In some embodiments, other interaction mechanisms may be used including, but not limited to, sliders, manual editing of XML, etc. For instance, a tag such as the following may be displayed including attributes:

-   -   <wnd app=‘java.exe’ cls=‘SunAwtFrame’ title=‘SwingSet2’     -   matching:title=‘fuzzy’ fuzzylevel:title=‘0.8’/>

where the attributes “matching:title” and “fuzzylevel:title” were added to the <wnd/> tag. The matching:title attribute has the value “fuzzy” to indicate that the value of the “title” attribute of the current tag will be fuzzy matched at runtime. The value of the fuzzylevel:title attribute indicates the desired fuzzy similarity threshold specified by the developer (in this example, 0.8). One of ordinary skill in the art will appreciate that there may be many other ways of encoding a fuzziness flag earmarking a selected attribute for fuzzy matching without deviating from the scope of the invention.

In some embodiments, the RPA designer application may provide RPA developers with the ability to search for attributes manually. For instance, RPA designer application 600 includes an attribute search field 630 that may perform a search for an attribute as the RPA developer types text. In certain embodiments, a list of matching attributes, if any, may appear with each character that is typed.

In this example, click activity 612 has been configured to click a certain checkbox. However, any suitable activity that seeks to interact with one or more graphical elements may be used and configured without deviating from the scope of the invention. The graphical element detection technique(s) for each suitable activity may be configured in this manner to perform the desired RPA actions. In certain embodiments, the RPA developer may choose to use only a single graphical element detection technique for one or more activities and multiple simultaneous graphical element detection techniques for one or more other activities. This provides a highly flexible RPA development tool.

FIGS. 7A and 7B illustrate another example graphical element detection technique configuration interface 700 for an RPA designer application, according to an embodiment of the present invention. As with graphical element detection technique configuration interface 600, graphical element detection technique configuration interface 700 includes an RPA workflow development pane 710 with a click activity 712, a graphical element detection technique configuration pane 720, and an attribute search field 730. However, in this embodiment, the other graphical element detection techniques are nested within a unified target technique 721. If unified target technique 721 is selected, as in FIG. 7A, a unified target approach will be used that applies the other techniques in series, in parallel, or at least one of the techniques for a period of time and then if no match is found in the period of time, employs at least one other technique in parallel.

If unified target technique 721 is de-selected, the developer may select and de-select desired techniques and/or attributes. A triangle 722 allows the RPA developer to show or hide a list of attributes for each graphical element detection technique. A graphical element detection technique selection checkbox 724 allows the RPA developer to select or de-select all of the attributes for a given graphical element detection technique at once. An attribute checkbox 726 allows the RPA developer to select or de-select a respective attribute. In some embodiments, fuzzy matching may be employed for the attributes. By selecting a fuzzy matching radio button 728, the RPA developer can cause a respective fuzzy matching threshold field 729 to appear.

FIG. 8 is a flowchart illustrating a process 800 for configuring an RPA workflow to perform graphical element detection using a combination of UI descriptor attributes from two or more graphical element detection techniques, according to an embodiment of the present invention. In some embodiments, process 800 may be performed by RPA designer applications 600 or 700 of FIG. 6 or 7A and 7B. The process begins with receiving an activity selection in an RPA workflow at 810 to be configured to perform graphical element detection using a combination of UI descriptor attributes from two or more graphical element detection techniques. In some embodiments, the types of graphical element detection techniques and/or the UI descriptor attributes are chosen automatically at 820 based on the action implemented by the activity (e.g., click, get text, hover, etc.), the graphical element type (e.g., button, text field, etc.), and/or the specific graphical element that is indicated by the RPA developer (i.e., which element the user picked on the screen and what other elements are present in the application). With respect to the specific graphical element, if the RPA developer clicks one OK button, for example, but there are two OK buttons on the screen, some attributes may be automatically added to distinguish between the two otherwise identical OK buttons. When using a UI tree, for instance, the UI tree is usually built in such a way that when an RPA developer indicates a graphical element on the screen, at least some of the attributes in the UI tree are different for that graphical element than for other graphical elements. The RPA designer application may receive selections of and/or modifications to the attributes for the graphical element detection techniques from the RPA developer at 830 in order to custom configure the activity.

The RPA designer application then configures the activity based on the chosen graphical element detection techniques and attributes at 840. If more activities are to be configured, the RPA developer may select another activity and the process returns to step 810. Once the desired activit(ies) are configured, the RPA designer application generates an RPA robot to implement an RPA workflow including the configured activit(ies) at 850. The process then ends or proceeds to FIG. 9.

FIG. 9 is a flowchart illustrating a process 900 for performing graphical element detection using a combination of UI descriptor attributes from two or more graphical element detection techniques, according to an embodiment of the present invention. In some embodiments, process 900 may be implemented at runtime by an RPA robot created via RPA designer applications 600 or 700 of FIG. 6 or 7A and 7B. The process begins with analyzing a UI (e.g., a screenshot, an image of an application window, etc.) to identify UI element attributes at 910. The UI element attributes may include, but are not limited to, images, text, relationships between graphical elements, a hierarchical representation of the graphical elements in the UI, etc. The identification may be performed via CV, OCR, API calls, analysis of text files (e.g., HTML, XML, etc.), a combination thereof, etc.

After the UI has been analyzed, the UI element attributes are compared to configured UI descriptor attributes of two or more graphical element detection techniques for an activity at 920. This may be based on the configuration of the activity in step 840 of FIG. 8, for example. If a UI element matching the attributes of the graphical element detection techniques is found at 930 via an exact match or a threshold match, the action associated with that activity involving the UI element is performed at 940 (e.g., clicking a button, entering text, interacting with a menu, etc.). If there are more activities at 950, the process proceeds to step 920 for the next activity. If, however, the UI element matching the attributes of the graphical element detection techniques is not found at 930, an exception is thrown at 960 and the process ends.

The process steps performed in FIGS. 8 and 9 may be performed by a computer program, encoding instructions for the processor(s) to perform at least part of the process(es) described in FIGS. 8 and 9 in accordance with embodiments of the present invention. The computer program may be embodied on a non-transitory computer-readable medium. The computer-readable medium may be, but is not limited to, a hard disk drive, a flash device, RAM, a tape, and/or any other such medium or combination of media used to store data. The computer program may include encoded instructions for controlling processor(s) of a computing system (e.g., processor(s) 510 of computing system 500 of FIG. 5) to implement all or part of the process steps described in FIGS. 8 and 9, which may also be stored on the computer-readable medium.

The computer program can be implemented in hardware, software, or a hybrid implementation. The computer program can be composed of modules that are in operative communication with one another, and which are designed to pass information or instructions to display. The computer program can be configured to operate on a general purpose computer, an ASIC, or any other suitable device.

It will be readily understood that the components of various embodiments of the present invention, as generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Thus, the detailed description of the embodiments of the present invention, as represented in the attached figures, is not intended to limit the scope of the invention as claimed, but is merely representative of selected embodiments of the invention.

The features, structures, or characteristics of the invention described throughout this specification may be combined in any suitable manner in one or more embodiments. For example, reference throughout this specification to “certain embodiments,” “some embodiments,” or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, appearances of the phrases “in certain embodiments,” “in some embodiment,” “in other embodiments,” or similar language throughout this specification do not necessarily all refer to the same group of embodiments and the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

It should be noted that reference throughout this specification to features, advantages, or similar language does not imply that all of the features and advantages that may be realized with the present invention should be or are in any single embodiment of the invention. Rather, language referring to the features and advantages is understood to mean that a specific feature, advantage, or characteristic described in connection with an embodiment is included in at least one embodiment of the present invention. Thus, discussion of the features and advantages, and similar language, throughout this specification may, but do not necessarily, refer to the same embodiment.

Furthermore, the described features, advantages, and characteristics of the invention may be combined in any suitable manner in one or more embodiments. One skilled in the relevant art will recognize that the invention can be practiced without one or more of the specific features or advantages of a particular embodiment. In other instances, additional features and advantages may be recognized in certain embodiments that may not be present in all embodiments of the invention.

One having ordinary skill in the art will readily understand that the invention as discussed above may be practiced with steps in a different order, and/or with hardware elements in configurations which are different than those which are disclosed. Therefore, although the invention has been described based upon these preferred embodiments, it would be apparent to those of skill in the art that certain modifications, variations, and alternative constructions would be apparent, while remaining within the spirit and scope of the invention. In order to determine the metes and bounds of the invention, therefore, reference should be made to the appended claims. 

1. A computer-implemented method for detecting graphical elements in a user interface (UI), comprising: providing, by a designer application, a selection interface that facilitates selection of graphical element detection techniques of a plurality of graphical element detection techniques for individual activities of a robotic process automation (RPA) workflow on a per-activity basis; receiving, by the designer application, a selection from the selection interface of an activity to be configured in the RPA workflow; receiving, by the designer application, selections of and/or modifications to one or more graphical element detection techniques of the plurality of graphical element detection techniques, one or more UI descriptor attributes of the one or more graphical element detection techniques, or a combination thereof, for the selected activity; and configuring the selected activity, by the designer application, based on the received selections and/or modifications.
 2. The computer-implemented method of claim 1, further comprising: automatically configuring one or more of the plurality of graphical element detection techniques, one or more of the UI descriptor attributes, or both, by the designer application.
 3. The computer-implemented method of claim 2, wherein the automatic configuration is performed by the designer application based on an action implemented by the activity, a type of the target graphical element, a presence of one or more other graphical elements in the UI, or a combination thereof.
 4. The computer-implemented method of claim 1, wherein the process of claim 1 is repeated for at least one additional activity in the RPA workflow.
 5. The computer-implemented method of claim 1, further comprising: generating an RPA robot configured to implement the configured activity, by the designer application.
 6. The computer-implemented method of claim 5, wherein the RPA robot is configured to use a subset of the UI descriptor attributes for at least one of the plurality of graphical element detection techniques.
 7. The computer-implemented method of claim 5, further comprising: analyzing a UI at runtime, by the RPA robot, to identify UI element attributes; comparing the UI element attributes to the configured UI descriptor attributes for the activity, by the RPA robot; and when a UI element matching the attributes of the plurality of graphical element detection techniques is found via an exact match or a threshold match: taking an action associated with the activity involving the UI element, by the RPA robot.
 8. The computer-implemented method of claim 7, wherein the UI attributes comprise images, text, relationships between graphical elements in the UI, a hierarchical representation of the graphical elements in the UI, or a combination thereof.
 9. The computer-implemented method of claim 7, wherein the process of claim 7 is repeated for at least one additional activity.
 10. The computer-implemented method of claim 1, wherein types of the UI descriptors comprise two or more of a selector, a computer vision (CV) descriptor, an image matching descriptor, and an optical character recognition (OCR) descriptor.
 11. A non-transitory computer-readable medium storing a computer program, the computer program configured to cause at least one processor to: provide a selection interface that facilitates selection of graphical element detection techniques of a plurality of graphical element detection techniques for individual activities of a robotic process automation (RPA) workflow on a per-activity basis; receive a selection from the selection interface of an activity to be configured in the RPA workflow; receive selections of and/or modifications to one or more graphical element detection techniques of the plurality of graphical element detection techniques, one or more UI descriptor attributes of the plurality of graphical element detection techniques, or a combination thereof; and configure the selected activity based on the received selections and/or modifications.
 12. The non-transitory computer-readable medium of claim 11, wherein the computer program is further configured to cause the at least one processor to: automatically configure one or more of the plurality of graphical element detection techniques, one or more of the UI descriptor attributes, or both.
 13. The non-transitory computer-readable medium of claim 12, wherein the automatic configuration is performed based on an action implemented by the activity, a type of the target graphical element, a presence of one or more other graphical elements in the UI, or a combination thereof.
 14. The non-transitory computer-readable medium of claim 11, wherein the process of claim 11 is repeated for at least one additional activity in the RPA workflow.
 15. The non-transitory computer-readable medium of claim 11, wherein the computer program is further configured to cause the at least one processor to: generate an RPA robot configured to implement the configured activity.
 16. The non-transitory computer-readable medium of claim 15, wherein the RPA robot is configured to use a subset of the UI descriptor attributes for at least one of the plurality of graphical element detection techniques.
 17. The non-transitory computer-readable medium of claim 11, wherein the types of UI descriptors comprise two or more of a selector, a computer vision (CV) descriptor, an image matching descriptor, and an optical character recognition (OCR) descriptor.
 18. A computing system, comprising: memory storing computer program instructions for detecting graphical elements in a user interface (UI); and at least one processor configured to execute the computer program instructions, wherein the computer program instructions are configured to cause the at least one processor to: provide a selection interface that facilitates selection of graphical element detection techniques of a plurality of graphical element detection techniques for individual activities of a robotic process automation (RPA) workflow on a per-activity basis; receive selections of multiple graphical element detection techniques of the plurality of graphical element detection techniques for an activity; and configure the activity based on the received graphical element detection technique selections.
 19. The computing system of claim 18, wherein the computer program instructions are further configured to cause the at least one processor to: automatically configure one or more of the plurality of graphical element detection techniques, one or more of the UI descriptor attributes, or both.
 20. The computing system of claim 18, wherein the automatic configuration is performed based on an action implemented by the activity, a type of the target graphical element, a presence of one or more other graphical elements in the UI, or a combination thereof.
 21. The computing system of claim 18, wherein the process of claim 18 is repeated for at least one additional activity in the RPA workflow.
 22. The computing system of claim 18, wherein the computer program instructions are further configured to cause the at least one processor to: generate an RPA robot configured to implement the configured activity.
 23. The computing system of claim 18, wherein the RPA robot is configured to use a subset of the UI descriptor attributes for at least one of the plurality of graphical element detection techniques. 